Import

df_csse <- (lubridate::today(tzone = "UTC") - 2) %>% 
  format("%m-%d-%Y") %>% 
  paste0(".csv") %>% 
  paste0("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/",
         .) %>% 
  readr::read_csv()

df_csse
df_csse %>% 
  skimr::skim()
Data summary
Name Piped data
Number of rows 3981
Number of columns 14
_______________________
Column type frequency:
character 4
numeric 9
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Admin2 712 0.82 3 41 0 1924 0
Province_State 171 0.96 3 44 0 576 0
Country_Region 0 1.00 2 32 0 192 0
Combined_Key 0 1.00 4 60 0 3981 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
FIPS 717 0.82 32419.86 18023.68 66.00 19052.50 30070.00 47041.50 99999.00 ▆▇▆▁▁
Lat 88 0.98 35.97 13.02 -52.37 33.27 37.95 42.21 71.71 ▁▁▁▇▁
Long_ 88 0.98 -71.99 53.64 -178.12 -96.60 -86.85 -77.49 178.06 ▁▇▁▁▁
Confirmed 0 1.00 28272.87 141401.94 0.00 974.00 2704.00 10591.00 3639501.00 ▇▁▁▁▁
Deaths 0 1.00 627.35 3558.87 0.00 15.00 48.00 168.00 107388.00 ▇▁▁▁▁
Recovered 0 1.00 15951.92 106204.67 0.00 0.00 0.00 0.00 2540293.00 ▇▁▁▁▁
Active 2 1.00 11698.08 91453.73 -1614614.00 773.50 2147.00 6417.50 3523341.00 ▁▇▁▁▁
Incident_Rate 89 0.98 7696.69 3814.12 0.00 5298.11 8130.74 10183.59 32387.39 ▅▇▁▁▁
Case_Fatality_Ratio 42 0.99 2.03 3.25 0.00 1.16 1.75 2.49 158.57 ▇▁▁▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
Last_Update 0 1 2020-08-04 02:27:56 2021-02-25 05:24:57 2021-02-25 05:24:57 8 
df_csse %>% 
  dplyr::mutate(Country_Region = forcats::as_factor(Country_Region)) %>%
  # .$Country_Region %>% levels()
  dplyr::filter(Country_Region == "Japan")
df_csse_ts <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv" %>% 
  readr::read_csv() %>% 
  dplyr::rename(Providence_State = `Province/State`, Country_Region = `Country/Region`) %>% 
  dplyr::select(-Lat, -Long) %>%
  tidyr::pivot_longer(cols = c(-Providence_State, -Country_Region),
  # tidyr::pivot_longer(cols = c(-Providence_State, -Country_Region, -Lat, -Long),
                      names_to = "date", values_to = "cum") %>% 
  dplyr::mutate(date = lubridate::mdy(date),
                Country_Region = forcats::as_factor(Country_Region))

df_csse_ts
df_csse_ts %>% 
  dplyr::filter(Country_Region == "US") 
# %>% 
#   dplyr::mutate(n = lagdiff(cum), ma7 = ma7(n), ma28 = ma28(n))
df_asia_daily <- df_csse_ts %>% 
  dplyr::filter(Country_Region %in% c("India", "Indonesia", "Japan",
                                      "Korea, South", "Mongolia", "Philippines",
                                      "Singapore", "Taiwan*", "Thailand",
                                      "Vietnam")) %>% 
  # dplyr::filter(is.na(Providence_State)) %>% 
  dplyr::arrange(date) %>% 
  dplyr::group_by(Country_Region) %>% 
  dplyr::mutate(n = lagdiff(cum), ma7 = ma7(n), ma28 = ma28(n)) %>% 
  dplyr::ungroup()

df_asia_daily

　

Visualize

亜細亜

香港

subset <- df_csse_ts %>% 
  dplyr::filter(Country_Region == "China" & Providence_State == "Hong Kong") %>% 
  dplyr::arrange(date) %>% 
  dplyr::group_by(Country_Region) %>% 
  dplyr::mutate(n = lagdiff(cum), ma7 = ma7(n), ma28 = ma28(n)) %>% 
  dplyr::ungroup()

title <- "【香港】陽性者数(単日)"
xlab <- ""
ylab <- "陽性者数"
sec_scale <- 50
dbreaks <- "2 month"
dlabels <- "%y-%m"
# dvline <- lubridate::as_date("2021-01-08")
ncol <- 2

subset %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7),
                       linetype = "solid", size = 0.25) +
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale)) +
    # ggplot2::geom_vline(xintercept = dvline, size = 0.2) + 
    ggplot2::scale_x_date(date_breaks = dbreaks, date_labels = dlabels) + 
    ggplot2::theme(legend.position = 'none') + 
    # ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") +
    # ggplot2::facet_wrap(~ key, ncol = ncol, scales = "fixed") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（棒）・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者数累計（太線）")) +
    ggplot2::labs(title = title, subtitle = subtitle, caption = caption,
                  x = xlab, y = ylab)

支那

欧州

df_Western_daily <- df_csse_ts %>% 
  dplyr::filter(Country_Region %in% c("France", "Germany", "Italy", "Spain",
                                      "Netherlands", "Norway", "Sweden", 
                                      "United Kingdom")) %>% 
  # tidyr::unite(col = "Country", Country_Region, Providence_State, sep = ", ") %>% 
  dplyr::filter(is.na(Providence_State)) %>% 
  dplyr::arrange(date) %>% 
  dplyr::group_by(Country_Region) %>% 
  dplyr::mutate(n = lagdiff(cum), ma7 = ma7(n), ma28 = ma28(n)) %>% 
  dplyr::ungroup()

df_Western_daily